





import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


__A							   =							{
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

__A							   =							{
    '169M': 768,
    '430M': 1024,
    '1B5': 2048,
    '3B': 2560,
    '7B': 4096,
    '14B': 5120,
}





def 	lowerCAmelCase_					(			__a						)	->						List[str]:







  """simple docstring"""





  lowerCamelCase__: Union[str, Any]         =list(state_dict.keys()						)
  for name in state_dict_keys:
    lowerCamelCase__: Union[str, Any]         =state_dict.pop(__a						)
    # emb -> embedding
    if name.startswith("emb."						):
      lowerCamelCase__: str         =name.replace("emb."      ,       "embeddings."						)
    # ln_0 -> pre_ln (only present at block 0)
    if name.startswith("blocks.0.ln0"						):
      lowerCamelCase__: Tuple         =name.replace("blocks.0.ln0"      ,       "blocks.0.pre_ln"						)
    # att -> attention
    lowerCamelCase__: Optional[int]         =re.sub(R"blocks\.(\d+)\.att"      ,       R"blocks.\1.attention"      ,       __a						)
    # ffn -> feed_forward
    lowerCamelCase__: List[Any]         =re.sub(R"blocks\.(\d+)\.ffn"      ,       R"blocks.\1.feed_forward"      ,       __a						)
    # time_mix_k -> time_mix_key and reshape
    if name.endswith(".time_mix_k"						):
      lowerCamelCase__: Optional[int]         =name.replace(".time_mix_k"      ,       ".time_mix_key"						)
    # time_mix_v -> time_mix_value and reshape
    if name.endswith(".time_mix_v"						):
      lowerCamelCase__: Tuple         =name.replace(".time_mix_v"      ,       ".time_mix_value"						)
    # time_mix_r -> time_mix_key and reshape
    if name.endswith(".time_mix_r"						):
      lowerCamelCase__: List[Any]         =name.replace(".time_mix_r"      ,       ".time_mix_receptance"						)

    if name != "head.weight":
      lowerCamelCase__: int         ='''rwkv.''' + name

    lowerCamelCase__: List[str]         =weight
  return state_dict





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a      ,       __a=None      ,       __a=None      ,       __a=False      ,       __a=None						)	->						int:







  """simple docstring"""





  if tokenizer_file is None:
    print("No `--tokenizer_file` provided, we will use the default tokenizer."						)
    lowerCamelCase__: Tuple         =50277
    lowerCamelCase__: Dict         =AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"						)
  else:
    lowerCamelCase__: Optional[int]         =PreTrainedTokenizerFast(tokenizer_file=__a						)
    lowerCamelCase__: List[Any]         =len(__a						)
  tokenizer.save_pretrained(__a						)

  # 2. Build the config
  lowerCamelCase__: Dict         =list(NUM_HIDDEN_LAYERS_MAPPING.keys()						)
  if size is None:
    # Try to infer size from the checkpoint name
    for candidate in possible_sizes:
      if candidate in checkpoint_file:
        lowerCamelCase__: Dict         =candidate
        break
    if size is None:
      raise ValueError("Could not infer the size, please provide it with the `--size` argument."						)
  if size not in possible_sizes:
    raise ValueError(F"""`size` should be one of {possible_sizes}, got {size}."""						)

  lowerCamelCase__: Optional[int]         =RwkvConfig(
      vocab_size=__a      ,       num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]      ,       hidden_size=HIDEN_SIZE_MAPPING[size]      ,       )
  config.save_pretrained(__a						)

  # 3. Download model file then convert state_dict
  lowerCamelCase__: Union[str, Any]         =hf_hub_download(__a      ,       __a						)
  lowerCamelCase__: List[Any]         =torch.load(__a      ,       map_location="cpu"						)
  lowerCamelCase__: str         =convert_state_dict(__a						)

  # 4. Split in shards and save
  lowerCamelCase__: Any         =shard_checkpoint(__a						)
  for shard_file, shard in shards.items():
    torch.save(__a      ,       os.path.join(__a      ,       __a						)						)

  if index is not None:
    lowerCamelCase__: List[str]         =os.path.join(__a      ,       __a						)
    # Save the index as well
    with open(__a      ,       "w"      ,       encoding="utf-8"						) as f:
      lowerCamelCase__: Dict         =json.dumps(__a      ,       indent=2      ,       sort_keys=__a						) + '''\n'''
      f.write(__a						)

    # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
    print(
        "Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model."						)
    lowerCamelCase__: List[Any]         =list(shards.keys()						)

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      lowerCamelCase__: List[Any]         =torch.load(os.path.join(__a      ,       __a						)						)
      torch.save({k: v.cpu().clone() for k, v in state_dict.items()}      ,       os.path.join(__a      ,       __a						)						)

  del state_dict
  gc.collect()

  if push_to_hub:
    if model_name is None:
      raise ValueError("Please provide a `model_name` to push the model to the Hub."						)
    lowerCamelCase__: Optional[Any]         =AutoModelForCausalLM.from_pretrained(__a						)
    model.push_to_hub(__a      ,       max_shard_size="2GB"						)
    tokenizer.push_to_hub(__a						)


if __name__ == "__main__":
     __A							   =							argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--repo_id", default=None, type=str, required=True, help="Repo ID from which to pull the checkpoint."
     )
     parser.add_argument(
         "--checkpoint_file", default=None, type=str, required=True, help="Name of the checkpoint file in the repo."
     )
     parser.add_argument(
         "--output_dir", default=None, type=str, required=True, help="Where to save the converted model."
     )
     parser.add_argument(
         "--tokenizer_file",
         default=None,
         type=str,
         help="Path to the tokenizer file to use (if not provided, only the model is converted).",
     )
     parser.add_argument(
         "--size",
         default=None,
         type=str,
         help="Size of the model. Will be inferred from the `checkpoint_file` if not passed.",
     )
     parser.add_argument(
         "--push_to_hub",
         action="store_true",
         help="Push to the Hub the converted model.",
     )
     parser.add_argument(
         "--model_name",
         default=None,
         type=str,
         help="Name of the pushed model on the Hub, including the username / organization.",
     )

     __A							   =							parser.parse_args()
     convert_rmkv_checkpoint_to_hf_format(
         args.repo_id,
         args.checkpoint_file,
         args.output_dir,
         size=args.size,
         tokenizer_file=args.tokenizer_file,
         push_to_hub=args.push_to_hub,
         model_name=args.model_name,
     )









'''simple docstring'''





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_UpperCamelCase						:						int  =					logging.get_logger(__name__)
def __UpperCAmelCase   (					A					:			Union[tf.Tensor, np.ndarray]		)    ->   List[int]:
			if isinstance(A			,       np.ndarray		):
						return list(tensor.shape		)

			UpperCAmelCase_      :       str          =       tf.shape(A		)

			if tensor.shape == tf.TensorShape(A		):
						return dynamic

			UpperCAmelCase_      :       Any          =       tensor.shape.as_list()

			return [dynamic[i] if s is None else s for i, s in enumerate(A		)]
def __UpperCAmelCase   (					A					:			tf.Tensor			,       A					:			Optional[int] = None			,       A					:			Optional[str] = None		)    ->   tf.Tensor:
			return tf.nn.softmax(logits=logits + 1e-9			,       axis=A			,       name=A		)
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			Union[str, Any]			,       A					:			List[str]			,       A					:			str=1e-5			,       A					:			Dict=-1		)    ->   List[Any]:
			# This is a very simplified functional layernorm, designed to duplicate
			# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
			# models in Transformers.

			if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(A			,       A		):
						raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''		)

			# Get mean and variance on the axis to be normalized
			UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       tf.nn.moments(A			,       axes=[axis]			,       keepdims=A		)

			if axis != -1:
						# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
						# on every dimension except axis
						UpperCAmelCase_      :       Tuple          =       [1] * inputs.shape.rank
						UpperCAmelCase_      :       int          =       shape_list(A		)[axis]
						UpperCAmelCase_      :       Dict          =       tf.reshape(A			,       A		)
						UpperCAmelCase_      :       Optional[Any]          =       tf.reshape(A			,       A		)

			# Compute layer normalization using the batch_normalization
			# function.
			UpperCAmelCase_      :       Dict          =       tf.nn.batch_normalization(
			    A			,       A			,       A			,       offset=A			,       scale=A			,       variance_epsilon=A			,       )
			return outputs
def __UpperCAmelCase   (					A					:			Dict			,       A					:			int=0			,       A					:			Optional[int]=-1		)    ->   str:
			# Replicates the behavior of torch.flatten in TF

			# If end_dim or start_dim is negative, count them from the end
			if end_dim < 0:
						end_dim += input.shape.rank
			if start_dim < 0:
						start_dim += input.shape.rank

			if start_dim == end_dim:
						return input

			UpperCAmelCase_      :       Any          =       tf.shape(A		)
			UpperCAmelCase_      :       Dict          =       tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]		)
			UpperCAmelCase_      :       Optional[int]          =       tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]			,       axis=0		)
			return tf.reshape(A			,       A		)
def __UpperCAmelCase   (					A					:			tf.Tensor		)    ->   tf.Tensor:
			if not isinstance(A			,       tf.Tensor		):
						UpperCAmelCase_      :       Optional[Any]          =       tf.convert_to_tensor(A		)  # Catches stray NumPy inputs
			if encoder_attention_mask.shape.rank == 3:
						UpperCAmelCase_      :       Any          =       encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.shape.rank == 2:
						UpperCAmelCase_      :       Tuple          =       encoder_attention_mask[:, None, None, :]
			# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
			# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
			# /transformer/transformer_layers.py#L270
			# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
			# encoder_extended_attention_mask.transpose(-1, -2))
			UpperCAmelCase_      :       int          =       (
			    tf.cast(1			,       encoder_attention_mask.dtype		) - encoder_extended_attention_mask
			) * encoder_extended_attention_mask.dtype.min

			return encoder_extended_attention_mask
def __UpperCAmelCase   (					A					:			tf.Tensor			,       A					:			int			,       A					:			str = "input_ids"		)    ->   None:
			tf.debugging.assert_less(
			    A			,       tf.cast(A			,       dtype=tensor.dtype		)			,       message=(
			        F"The maximum value of {tensor_name} ({tf.math.reduce_max(A		)}) must be smaller than the embedding "
			        F"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
			    )			,       )
def __UpperCAmelCase   (					A					:			Dict			,       A					:			Tuple			,       A					:			List[str]		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       List[Any]          =       6_4_5_1_2
			# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
			# because in that case even chunking the array would not make the saving
			# possible.
			UpperCAmelCase_      :       Tuple          =       [x for x in data if len(A		) > HDF5_OBJECT_HEADER_LIMIT]

			# Expecting this to never be true.
			if bad_attributes:
						raise RuntimeError(
						    '''The following attributes cannot be saved to HDF5 file because '''
						    F"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
						    F"bytes: {bad_attributes}"		)

			UpperCAmelCase_      :       Union[str, Any]          =       np.asarray(A		)

			UpperCAmelCase_      :       Optional[int]          =       1
			UpperCAmelCase_      :       str          =       np.array_split(A			,       A		)

			# This will never loop forever thanks to the test above.
			while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data		):
						num_chunks += 1
						UpperCAmelCase_      :       Dict          =       np.array_split(A			,       A		)

			if num_chunks > 1:
						for chunk_id, chunk_data in enumerate(A		):
									UpperCAmelCase_      :       int          =       chunk_data
			else:
						UpperCAmelCase_      :       List[Any]          =       data
def __UpperCAmelCase   (					A					:			int			,       A					:			Optional[Any]		)    ->   Tuple:
			if name in group.attrs:
						UpperCAmelCase_      :       Optional[int]          =       [n.decode('''utf8'''		) if hasattr(A			,       '''decode'''		) else n for n in group.attrs[name]]
			else:
						UpperCAmelCase_      :       Optional[int]          =       []
						UpperCAmelCase_      :       Optional[int]          =       0
						while "%s%d" % (name, chunk_id) in group.attrs:
									data.extend(
									    [n.decode('''utf8'''		) if hasattr(A			,       '''decode'''		) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]		)
									chunk_id += 1
			return data







def __UpperCAmelCase   (					A					:			Tuple		)    ->   str:

			def _expand_single_ad_tensor(A					:			int		):
						if isinstance(A			,       tf.Tensor		) and t.shape.rank == 1:
									return tf.expand_dims(A			,       axis=-1		)
						return t

			return tf.nest.map_structure(_expand_single_ad_tensor			,       A		)


